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In this paper, the authors will study the clustering problem for uncertain data. When information about the data uncertainty is available, it can be leveraged in order to improve the quality of the underlying results. They will provide a survey of the different algorithms for clustering uncertain data. This includes recent algorithms for clustering static data sets, as well as the algorithms for clustering uncertain data streams. Many data sets which are collected often have uncertainty built into them. In many cases, the underlying uncertainty can be easily measured and collected.
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